ArticleCommunications biology2025
Reference-informed evaluation of batch correction for single-cell omics data with overcorrection awareness.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Single-cell mapping of homocysteine-induced perturbations in avian embryogenesis using LMO and BD rhapsody transcriptomics.iScience · 2026Article
- Practical Impact of Imputation and Batch-Effect Correction for Proteomics/Peptidomics Differential-Abundance Analysis.Proteomics · 2026Article
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Gut microbiota and osteoarthritis: mechanisms and translation.Frontiers in immunology · 2026Review
- Development and comparison of evaluation metrics for batch correction reveals performance differences.Bioinformatics advances · 2026Article
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
- Statistical Methods for Multi-Omics Analysis in Neurodevelopmental Disorders: From High Dimensionality to Mechanistic Insight.Biomolecules · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Batch effect correction (BEC) is fundamental to integrate multiple single-cell RNA sequencing datasets, and its success is critical to empower in-depth interrogation for biological insights. However, no simple metric is available to evaluate BEC performance with sensitivity to data overcorrection, which erases true biological variations and leads to false biological discoveries. Here, we propose RBET, a reference-informed statistical framework for evaluating the success of BEC. Using extensive simulations and six real data examples including scRNA-seq and scATAC-seq datasets with different numbers of batches, batch effect sizes and numbers of cell types, we demonstrate that RBET evaluates the performance of BEC methods more fairly with biologically meaningful insights from data, while other methods may lead to false results. Moreover, RBET is computationally efficient, sensitive to overcorrection and robust to large batch effect sizes. Thus, RBET provides a robust guideline on selecting case-specific BEC method, and the concept of RBET is extendable to other modalities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.